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Teach the BranchProbabilityInfo analysis pass to read any metadata
encoding of probabilities. In the absense of metadata, it continues to fall back on static heuristics. This allows __builtin_expect, after lowering through llvm.expect a branch instruction's metadata, to actually enter the branch probability model. This is one component of resolving PR2577. llvm-svn: 142492
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@ -13,6 +13,8 @@
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#include "llvm/Constants.h"
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#include "llvm/Constants.h"
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#include "llvm/Instructions.h"
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#include "llvm/Instructions.h"
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#include "llvm/LLVMContext.h"
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#include "llvm/Metadata.h"
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#include "llvm/Analysis/BranchProbabilityInfo.h"
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#include "llvm/Analysis/BranchProbabilityInfo.h"
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#include "llvm/Analysis/LoopInfo.h"
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#include "llvm/Analysis/LoopInfo.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/Debug.h"
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@ -117,6 +119,9 @@ public:
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: BP(BP), LI(LI) {
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: BP(BP), LI(LI) {
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}
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}
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// Metadata Weights
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bool calcMetadataWeights(BasicBlock *BB);
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// Return Heuristics
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// Return Heuristics
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bool calcReturnHeuristics(BasicBlock *BB);
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bool calcReturnHeuristics(BasicBlock *BB);
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@ -133,6 +138,36 @@ public:
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};
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};
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} // end anonymous namespace
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} // end anonymous namespace
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// Propagate existing explicit probabilities from either profile data or
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// 'expect' intrinsic processing.
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// FIXME: This doesn't correctly extract probabilities for switches.
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bool BranchProbabilityAnalysis::calcMetadataWeights(BasicBlock *BB) {
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BranchInst *BI = dyn_cast<BranchInst>(BB->getTerminator());
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if (!BI || !BI->isConditional())
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return false;
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MDNode *WeightsNode = BI->getMetadata(LLVMContext::MD_prof);
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if (!WeightsNode || WeightsNode->getNumOperands() < 3)
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return false;
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// Pull the weights out of the metadata. Note that the zero operand is the
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// name.
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ConstantInt *Weights[] = {
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dyn_cast<ConstantInt>(WeightsNode->getOperand(1)),
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dyn_cast<ConstantInt>(WeightsNode->getOperand(2))
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};
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if (!Weights[0] || !Weights[1])
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return false;
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uint32_t WeightLimit = getMaxWeightFor(BB);
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BP->setEdgeWeight(BB, BI->getSuccessor(0),
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Weights[0]->getLimitedValue(WeightLimit));
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BP->setEdgeWeight(BB, BI->getSuccessor(1),
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Weights[1]->getLimitedValue(WeightLimit));
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return true;
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}
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// Calculate Edge Weights using "Return Heuristics". Predict a successor which
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// Calculate Edge Weights using "Return Heuristics". Predict a successor which
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// leads directly to Return Instruction will not be taken.
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// leads directly to Return Instruction will not be taken.
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bool BranchProbabilityAnalysis::calcReturnHeuristics(BasicBlock *BB){
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bool BranchProbabilityAnalysis::calcReturnHeuristics(BasicBlock *BB){
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@ -341,6 +376,9 @@ bool BranchProbabilityAnalysis::runOnFunction(Function &F) {
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for (Function::iterator I = F.begin(), E = F.end(); I != E; ) {
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for (Function::iterator I = F.begin(), E = F.end(); I != E; ) {
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BasicBlock *BB = I++;
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BasicBlock *BB = I++;
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if (calcMetadataWeights(BB))
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continue;
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if (calcLoopBranchHeuristics(BB))
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if (calcLoopBranchHeuristics(BB))
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continue;
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continue;
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@ -22,3 +22,28 @@ body:
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exit:
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exit:
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ret i32 %sum
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ret i32 %sum
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}
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}
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define i32 @test2(i32 %i, i32 %a, i32 %b) {
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; CHECK: Printing analysis {{.*}} for function 'test2'
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; CHECK: entry = 1024
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entry:
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%cond = icmp ult i32 %i, 42
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br i1 %cond, label %then, label %else, !prof !0
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; The 'then' branch is predicted more likely via branch weight metadata.
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; CHECK: then = 963
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then:
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br label %exit
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; CHECK: else = 60
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else:
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br label %exit
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; FIXME: It may be a bug that we don't sum back to 1024.
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; CHECK: exit = 1023
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exit:
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%result = phi i32 [ %a, %then ], [ %b, %else ]
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ret i32 %result
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}
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!0 = metadata !{metadata !"branch_weights", i32 64, i32 4}
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